近年來，生物醫學資訊(Bio-Medical Informatics)已成為新世紀科技發展的趨勢。『生物醫學資訊』顧名思義就是生物、醫學與資訊科學的結合。其主要是透過數學運算、統計與電腦分析等方法來研究生命科學的學問。因此所涵蓋與應用的層面極為廣泛，包括基因、醫療、保健與藥學等相關領域。

　　本論文把焦點集中在聽覺細胞的誘發特徵分析與行為模擬上。在聽覺細胞的誘發特徵分析中，我們提出一套可以求得調頻聲專一反應(FM Specialized)細胞於頻時受區(Spectro-Temporal Receptive Field, STRF)中誘發特徵(Triggering Feature)之正確斜率的方法。對於聽覺細胞的行為模擬，我們也提出一個系統模型可以模擬老鼠聽覺細胞的響應。

　　我們已知聲音訊號包含兩個主要的時變成份：調頻(FM)訊號及調幅(AM)訊號，並且已在中腦及腦皮層發現大量的調頻聲專一反應細胞。因此藉由研究調頻聲專一反應細胞的誘發特徵來瞭解語音處理機制是很重要的。通常我們會藉由平均造成細胞響應的隨機訊號來求得聽覺細胞的頻時受區，並用其描繪中腦細胞對調頻訊號的反應特性。但由於細胞響應會隨著時間變化，所以此方法會在頻時受區中誤將響應的變動誤差錯當成刺激訊號的變動誤差。為了求得正確的誘發特徵，我們提出一個方法來最小化此誤差。如此我們就可以在頻時受區得到正確的FM誘發特徵。實驗結果發現兩組資料所產生的FM訊號誘發點是完全相同的。因此我們可以確認我們所提出的方法可以有效的調整反應變動誤差並可以正確的描繪出細胞於頻時受區中的誘發特徵。

　　此外我們也建立了一個模型來模擬聽覺細胞對於複雜聲音的反應。模型是由一個多層次分類處理、兩個FIR類神經網路模組及一個最大值類神經網路模組所組成。實驗資料是由一組虛擬亂數調頻訊號(Pseudo Random FM tone)以及其傳送到大白鼠中腦聽覺細胞中之響應所組成。多層次分類處理會根據其反應分佈情形使用多層次高斯濾波器將反應資料區分為強反應(Strong Responses)及弱反應(Weak Responses)，接著兩個FIR類神經網路模組會獨立的訓練這兩類的細胞響應，然後它們的輸出會連接到最大值類神經網路模組以產生最後的輸出。訓練完成後，我們使用同一顆細胞對於另一組調頻波形的響應當成測試資料來測試此模型的效率。最後我們提出兩種評估效率的方法，一種是根據點對點的方式來評估模型輸出與實際輸出的相似度，另外一種方法是根據響應的範圍來判定相似度。結果顯示我們所提出的方法可以得到令人滿意的結果。

　In recent years, Biomedical Informatics has become a new trend of science and technology. Biomedical Informatics is bringing together researchers from bioinformatics, medical informatics and computer science. The principal of this subject is using mathematical computation, statistics and computer analysis for life sciences research. Therefore, their application is very extensively, including gene, medical treatment, medicine and so on.

　In this dissertation, we focus on the auditory cell’s triggering feature analysis and behavior modeling. For triggering feature analysis, we proposed a method for determining the triggering features with accurate FM slope in STRF. For the behavior modeling, we proposed a novel system to simulate responses of auditory neurons of rats to acoustic signals.

　It is well known that speech sounds contain two major time-varying components, FM and AM signals. It is also clear that many FM-sensitive neurons appear in significant proportions first at the auditory midbrain and subsequently at the auditory cortex. To understand the mechanisms of speech sound coding, detailed knowledge on the triggering features of FM sensitive neurons is of obvious importance. Sensitivity of central auditory neurons to frequency modulated (FM) sound is often characterized based on spectro-temporal receptive field (STRF), which is generated by spike-trigger averaging a random stimulus. Due to the inherent property of time variability in neural response, this method erroneously represents the response jitter as stimulus jitter in the STRF. To reveal the trigger features more clearly, we have implemented a method that minimizes this error.

　We also propose a computational model to simulate central auditory responses to complex sounds. It consists of a multi-scale classification process, and an arti-ficial neural network composed of two modules of finite impulse response (FIR) neural networks connected to a maximum network. Electrical activities of single auditory neurons were recorded at the rat midbrain in response to a repetitive pseudo-random frequency modulated (FM) sound. The multi-scale classification process divides the training dataset into either strong or weak response using a multiple-scale Gaussian filter that based on response probability. Two modules of FIR neural network are then independently trained to model the two types of responses. This caters for the possible differences in neuronal circuitry and transmission delay. Their outputs are connected to a maximum network to generate the final output. After training, we use a different set of FM responses collected from the same neuron to test the performance of the model. Two criteria are adopted for assessment. One measures the matching of the modeled output to the actual output on a point-to-point basis. Another measures the matching of bulk responses between the two. Results show that the proposed model predicts the responses of central auditory neurons satisfactorily.

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